UVM Methodology for Industry-Specific Applications Using AI in Hardware Verification
Abstract
These increasing sophistications of modern System-on-Chip (SoC), semiconductor, automotive, telecommunications and embedded systems have resulted in severe hardware verification issues, especially in achieving full coverage, reducing the connotation timeline, and uncovering difficult-to-find system defects. Universal Verification Methodology (UVM) provides a setting where standardized and re-usable functional verification takes place, however standard UVM-based verification often heavily depends on manually crafted test-cases and minimal helpers in order to set-up constrained-random stimulus. This work proposes an AI-powered system of UVM method to confirm the hardware specific to industries, which includes the implementation of artificial intelligence and machine learning technologies into the key phases of the UVM verification life cycle. The proposed framework also consists of AI-assisted code coverage, AI assisted test generation, failure classification, verification prioritization and adaptive optimization of stimulus in a modular UVM. Configurable verification components are used to represent industry-specific requirements, and allow the framework to be used in automotive, communication, consumer electronics, industrial, and safety-critical hardware applications. The framework is a continual process of analyzing results of simulations and coverage data to detect verification gaps and dynamically tune the further test cases. This strategy sets up a dynamic verification framework that integrates the reusability and standardization of UVM with the analytical powers of AI. The suggested methodology provides a systematic foundation of improving the performance of the verification, convergence of coverage and fault detection and scaling by heterogeneous hardware verification environments.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 4 No. 3 (2021): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
5731-5744 |
Published |
May 11, 2021 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Kaushik Velapa Reddy (%2021). UVM Methodology for Industry-Specific Applications Using AI in Hardware Verification. International Journal of Science, Research and Technology , Vol. 4 No. 3 (2021): International Journal of Science, Research and Technology (IJSRAT) , pp. 5731-5744. https://doi.org/10.15662/IJSRAT.2021.0403002 |
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